Text

theses

Endodontically treated teeth are often structurally compromised and require proper prosthodontic restoration following endodontic therapy to ensure long-term success. As a result and as the number of endodontic procedures in contemporary dentistry have steadily increased in the past decade, tooth restoration is becoming an integral part of restorative practice in everyday dentistry. Although the subject has been widely researched and published in dental literature, the topic of best way to restore root canal treated teeth still remains an area prone to high error rates in decision-making. Complexity of the domain is largely due to multi-factorial considerations and need for evaluation of a wide range of restorative techniques of varying intricacies. Determining the best restorative treatment plan following endodontic therapy requires sound knowledge of principles that span across multiple dental disciplines of endodontics, orthodontics, periodontics, and prosthodontics. Memorization of a large and ever-increasing number of decision rules to be meaningfully used at the point-of-care can be arduous, especially for dental students and less experienced clinicians. Clinical experts with their knowledge and years of experience treating patients can help in the process, but may not always be around to provide assistance. Misdiagnosis and mistreatment of such teeth can lead to adverse clinical consequences and significant inconvenience to the patient as well as financial implications both for the patient and provider. To address this problem, we have developed a clinical decision support and training system based on expert knowledge and evidence-based guidelines. Using Corvid expert system development framework and careful consideration of factors necessary for success of clinical decision support systems, we have developed a working prototype of a web-based, interactive system that can be launched at the operatory and can be easily integrated into providers’ workflow. One of the important goals of the system is to train users to think holistically like an expert while problem-solving and planning restorative treatment. Based on information entered by the user, system comes up with recommendations, alerts and treatment prognosis. Treatment options provided with justifying explanations can help clinicians provide decision-making rationale to the patients, better inform and educate them, and thus better manage their expectations and ensure their satisfaction and compliance. Since the knowledge base of our system is developed using expert guidelines that are known to be effective, we expect to see improved restorative treatment outcomes for endodontically treated teeth.

ETD_6013

doi:10.7282/T35T3N6M

Ph.D.

Includes bibliographical references

by Anjali B. Shah

ETD doctoral

The author owns the copyright to this work.